Accurate characterization of snow-covered area (SCA) and snow water equivalent (SWE) in complex terrain is needed to improve estimation of streamflow timing and volume, and is important for land surface modeling. Direct field observations of SWE, SCA and atmospheric forcing inputs for models of snow accumulation and ablation are typically sparsely sampled in space. Satellite imagery is, therefore, a critical tool for verification and confirmation of snow model estimates of SCA. The Landsat system provides snow-covered area estimates at a spatial resolution of 30 m with a 16-day return interval, while daily estimates of SCA and fractional SCA (fSCA) are available at 500 m from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study describes and tests a linear model to downscale MODIS MOD10A1 fSCA (500 m) data to higher-resolution (30 m) spatially explicit binary SCA estimates. The algorithm operates on the assumption that two variables, potential insolation and elevation, control differential ablation of snow cover throughout spring melt at 30 m to 500 m scales. The model downscales daily 500 m fSCA estimates from MODIS to provide daily SCA estimates at a spatial resolution of 30 m, using limited Landsat SCA for calibration and independent Landsat SCA estimates for validation. Downscaled SCA estimates demonstrate statistically significant improvement from randomly generated model ensembles, indicating that insolation and elevation are dominant factors controlling the snow cover distribution in the semi-arid, mountainous region in southwestern Idaho, USA where this study is performed. Validation is performed with Landsat data not used for calibration, and is also performed using Landsat 500 m aggregate fSCA instead of MODIS fSCA as an ideal case. Downscaled estimates show reasonable accuracy (test metric outperforms random ensembles at p = 0.01 significance level for multiple ranges of snow cover) with only one calibrated parameter.